Abstract

The increasing popularity of wireless services and devices necessitates high bandwidth requirements; however, spectrum resources are not only limited but also heavily underutilized. Multiple license channels that support the same levels of QoS are desirable to resolve the problems posed by the scarcity and inefficient use of spectrum resources in multi-channel cognitive radio networks (MCRNs). One reason is that multimedia services and applications have distinct, stringent QoS requirements. However, due to a lack of coordination between primary and secondary users, identifying the QoS levels supported over available licensed channels has proven to be problematic and has yet to be attempted. This article presents a novel Bayesian non-parametric channel clustering scheme, which identifies the QoS levels supported over available license channels. The proposed scheme employs the infinite Gaussian mixture model and collapsed Gibbs sampler to identify the QoS levels from the feature space of the bit rate, packet delivery ratio, and packet delay variation of licensed channels. Moreover, the real measurements of wireless data traces and comparisons with baseline clustering schemes are used to evaluate the performance of the proposed scheme.

abstract = "The increasing popularity of wireless services and devices necessitates high bandwidth requirements; however, spectrum resources are not only limited but also heavily underutilized. Multiple license channels that support the same levels of QoS are desirable to resolve the problems posed by the scarcity and inefficient use of spectrum resources in multi-channel cognitive radio networks (MCRNs). One reason is that multimedia services and applications have distinct, stringent QoS requirements. However, due to a lack of coordination between primary and secondary users, identifying the QoS levels supported over available licensed channels has proven to be problematic and has yet to be attempted. This article presents a novel Bayesian non-parametric channel clustering scheme, which identifies the QoS levels supported over available license channels. The proposed scheme employs the infinite Gaussian mixture model and collapsed Gibbs sampler to identify the QoS levels from the feature space of the bit rate, packet delivery ratio, and packet delay variation of licensed channels. Moreover, the real measurements of wireless data traces and comparisons with baseline clustering schemes are used to evaluate the performance of the proposed scheme.",

N2 - The increasing popularity of wireless services and devices necessitates high bandwidth requirements; however, spectrum resources are not only limited but also heavily underutilized. Multiple license channels that support the same levels of QoS are desirable to resolve the problems posed by the scarcity and inefficient use of spectrum resources in multi-channel cognitive radio networks (MCRNs). One reason is that multimedia services and applications have distinct, stringent QoS requirements. However, due to a lack of coordination between primary and secondary users, identifying the QoS levels supported over available licensed channels has proven to be problematic and has yet to be attempted. This article presents a novel Bayesian non-parametric channel clustering scheme, which identifies the QoS levels supported over available license channels. The proposed scheme employs the infinite Gaussian mixture model and collapsed Gibbs sampler to identify the QoS levels from the feature space of the bit rate, packet delivery ratio, and packet delay variation of licensed channels. Moreover, the real measurements of wireless data traces and comparisons with baseline clustering schemes are used to evaluate the performance of the proposed scheme.

AB - The increasing popularity of wireless services and devices necessitates high bandwidth requirements; however, spectrum resources are not only limited but also heavily underutilized. Multiple license channels that support the same levels of QoS are desirable to resolve the problems posed by the scarcity and inefficient use of spectrum resources in multi-channel cognitive radio networks (MCRNs). One reason is that multimedia services and applications have distinct, stringent QoS requirements. However, due to a lack of coordination between primary and secondary users, identifying the QoS levels supported over available licensed channels has proven to be problematic and has yet to be attempted. This article presents a novel Bayesian non-parametric channel clustering scheme, which identifies the QoS levels supported over available license channels. The proposed scheme employs the infinite Gaussian mixture model and collapsed Gibbs sampler to identify the QoS levels from the feature space of the bit rate, packet delivery ratio, and packet delay variation of licensed channels. Moreover, the real measurements of wireless data traces and comparisons with baseline clustering schemes are used to evaluate the performance of the proposed scheme.